Method for the determination of parameters of a seat passenger

ABSTRACT

A method for the determination of a physical property parameter of a seat passenger includes getting a reading of at least two parameters from an occupancy sensor; plotting a probability vector which shows, for each value of the physical property, the probability to cause the readings of the at least two parameters; and correlating the physical property parameter to the range of values of the probability vector with the highest probabilities.

The present invention relates to a method for the determination ofseveral parameters relating to an occupancy status of a vehicle seat,such as the weight and/or the stature or build of a passenger sitting inthe vehicle seat.

In order to protect the lives of passengers during a traffic accident,modern vehicles are generally fitted with a protection system comprisingseveral airbags and seat belt pretensioners, which are used to absorbthe energy of a passenger released during the collision due to theaccident. It is clear that such systems are most effective when they arewell adapted to the specific requirements of each passenger, i.e. to theweight and/or the size of the passenger. That is whymicroprocessor-controlled protection systems have been designed whichprovide several operational modes, for example allowing an adaptation ofthe instant at which airbags are deployed and their volume, of theinstant at which safety belts are released after the collision, etc, asa function of the build of the passenger and the position of thepassenger on the seat.

In order to enable the control microprocessor to select the optimumoperational mode for a given passenger, it is therefore necessary tohave available a method and a device for detecting the build or bodilyform of the passenger which determines the size and/or the weight and/orthe position of the passenger and which indicates this to the controlcircuit of the protection system.

For this purpose, the patent U.S. Pat. No. 5,232,243 describes a devicefor detecting the weight of a passenger which comprises severalindividual force sensors arranged in a matrix array in the vehicle seatcushion. The force sensors have an electric resistance that varies withthe applied force and are known by the abbreviation FSR (force sensingresistor). The resistance of each sensor is measured individually and,by adding the forces corresponding to the values of these resistances,an indication is obtained of the total force exerted, i.e. of the weightof the passenger. In other words, the method used in U.S. Pat. No.5,232,243 consists of directly associating a specific weight to aspecific reading of the sensor.

However, the total weight of a passenger does not act solely on thesurface of the seat, since part of the weight is supported by thepassenger's legs, which rest on the bottom of the vehicle, and anotherpart rests on the back of the seat. In addition, the ratios between thevarious parts vary considerably with the passenger's position on theseat, which causes the total force measured by the individual forcesensors not to correspond to the real weight of the passenger but toexperience very large variations depending on the passenger's posture onthe seat. This means on the other hand, that the same reading of thesensor, i.e. the same distribution of individually measured forces inthe case of a sensor comprising individual force sensors, can be causedby the presence of passengers having rather different physicalproperties. Hence there is a risk of wrong classification of a specificpassenger, which will cause the restraint system to be deployed in anon-adapted mode.

OBJECT OF THE INVENTION

The object of the present invention is to provide an improved method forthe determination of one or more parameters of a seat passenger, whichreduces the above-described risk.

GENERAL DESCRIPTION OF THE INVENTION

In order to overcome the above-mentioned problems, the present inventionproposes a method for the determination of a physical property parameterof a seat passenger comprising the steps of:

-   a) getting a reading of a first parameter from an occupancy sensor;-   b) plotting a probability vector, said probability vector showing,    for each value of the physical property, the probability to cause    said reading of said first parameter;-   c) correlating said physical property parameter to the range of    values of said probability vector with the highest probabilities.

In contrast to the known methods for determining a physical property ofa passenger, the present method does not directly associate a specificphysical property to a specific reading of the sensor but correlates thereading to the entire range of physical property values, which mightcause a specific sensor reading. It follows that the present method doesnot only consider a correctly seated passenger but also a passengerhaving a different physical property but seated in a non optimalposition. The risk of misclassification of the occupancy status isaccordingly reduced.

It should be noted that the present invention is not limited to thedetermination of the weight of a passenger, but can also be used todetermine the size or the build of a passenger sitting on the seat.

In order to improve the certainty of the determination of the physicalproperty, i.e. the actual classification of the occupancy status, themethod is preferably based on the evaluation of several parameters ofthe occupancy sensor. In this case, the method for the determination ofa physical property parameter of a seat passenger comprises the stepsof:

-   a) getting a reading of at least two parameters from an occupancy    sensor;-   b) plotting a probability vector, said probability vector showing,    for each value of the physical property, the probability to cause    said readings of said at least two parameters;-   c) correlating said physical property parameter to the range of    values of said probability vector with the highest probabilities.

The probability vector plotted in step b) shows the combinedprobabilities of each physical property value to cause the actualreadings of the different parameters. In other words, the present methodsuperposes the probability ranges of all the different parameters toobtain a final probability curve. It should be noted that the differentparameters which are evaluated by the method can be chosen from thefollowing groups:

-   anthropometric parameters, such as the distance between the centers    of force in two adjacent seat parts (IW) or the gradient of the    force between the center of the seat and an outer border (DPVratio);-   parameters based on absolute force, e.g. the sum of all measured    individual forces (SumTDPV) or the number of activated cells    (Activated Cells);-   form recognition parameters, such as the form and the size of the    occupied surface (Pattern Recognition).

In a preferred embodiment of the method, the step of plotting aprobability vector comprises the steps of:

-   a) initializing the probability vector by setting the probability    for the entire range of values of said physical property to a    specific value; and-   b) for each parameter, reducing the probability for each physical    property value for which the respective parameter is outside of    calibration curves, said calibration curves showing the maximum and    minimum parameter values for each physical property value.

As there is a dispersion of the parameter value between differentpersons having the same physical property and between differentpositions of one same person, the calibration curves determine themaximum and minimum reading of a specific parameter for each physicalproperty value. Such calibration curves can be obtained by getting thedifferent readings of each specific parameter for different personshaving the same physical property and for different positions on theseat. This step may be repeated for at least two different physicalproperty values. After the data are gathered, the calibration curves(maximum or minimum) can be plotted e.g. by interpolation between themaximum parameter values or the minimum parameter values respectively.The two curves obtained constitute an envelope for each given parameter,which corresponds to the total min-max spread for the range of thephysical property to be determined. A given parameter value is likely tobe caused by a person having e.g. a specific weight, if the parameterlies between the maximum and minimum calibration curve for this weight,i.e. if the parameter is inside the envelope.

If however, the parameter value lies outside the envelope for thisspecific weight, the probability for this parameter value having beencaused by a person of the specific weight is small. This means that theprobability in the probability vector of this weight value has to bereduced with respect to those weight values, for which the actualparameter values lies within the envelope. This reduction of theprobability can be done either by. multiplying the probability (vectorvalue) of the weight or size, where the actual parameter is outside thecorresponding envelope by a given border (<1). Alternatively, theprobability lowering method could consist in a subtraction. However inthis case it must be ensured that the probability doesn't fall below 0%or below the resolution, so that the different probability steps stillcan be distinguished.

In a very simple embodiment of the invention, the present method feedsthe determined range of physical property values with the highestprobability to the control circuit of a secondary restraint system. Thiscontrol system then switches the restraint system in a deployment mode,which is considered to be suitable for the entire determined range ofthe physical property. In a morte preferred embodiment of the invention,the correlation of said physical property parameter to said range ofvalues with the highest probabilities comprises the steps of

-   a) calculating an average physical property value from the range of    values of said probability vector with the highest probability;.-   b) setting said physical property parameter to be determined to    equal said average physical property value.

The output of such a method consists of an average physical propertyvalue which will cause the control circuit of the secondary restraintsystem to switch into a specific deployment modus.

In a method in which a plurality of parameters are considered, theranges of physical property values adjacent to the range with thehighest probability, i.e. the ranges with the second highestprobability, may also have a rather high. probability to cause theactual readings of the different parameters. This means that theprobability of the actual physical property to lie within these rangesis still rather high. In order not to discriminate these probabilities,said step of correlating said physical property parameter to said rangeof values with the highest probabilities in a more preferred embodimentof the method comprises the steps of

-   a) calculating a first average physical property value from the    range of values of said probability vector with the highest    probability;-   b) calculating a second average physical property value from range    of values of said probability vector for which the probability is    equal or higher than a second highest probability;-   c) setting said physical property parameter to be determined to    equal a rounded average of said first and second average physical    property value.

It should be noted, that the type of sensor from which the differentparameters are obtained is not relevant to the present invention. Infact, the method can be used with any type of seat occupancy sensors,such as pressure sensitive seat sensors and/or a capacitive sensors.

It will further be appreciated, that the present method can be combinedwith a temperature compensation method, which eliminates the influenceof the temperature on the readings of the specific parameters. In fact,because of variations with temperature of the characteristics of thesystem, the individual readings of the different sensors depend on theambient temperature in the vehicle. If the method is combined with asuitable temperature compensation, such influence of the temperature onthe parameter readings and by that on the determination of the actualphysical property can be advantageously reduced.

DETAILED DESCRIPTION WITH RESPECT TO THE FIGURES

The present invention will be more apparent from the followingdescription of a not limiting embodiment with reference to the attacheddrawings, wherein

FIG. 1: shows a bloc diagram of a weight estimation module;

FIG. 2: shows a diagram of the envelopes of one parameter vs. weight(physical property);

FIG. 3: illustrates the principle of the probability vector;

FIG. 4: shows a probability vector including several parameters.

FIG. 1 shows a bloc diagram of a weight estimation module employing themethod of the present invention. While the shown method is used todetermine the weight of the passenger, it will be clear to the oneskilled in the art, that an analogous method can be used to determinethe size or build of the passenger. The method for determining theweight of a seat passenger may be based one or more of the followingtypes of parameters of a seat occupancy sensor (OC Profiles):

-   anthropometric parameters, such as the distance between the centers    of force in two adjacent seat parts (IW) or the gradient of the    force between the center of the seat and an outer border (DPVratio);-   parameters based on absolute force, e.g. the sum of all measured    individual forces (SumTDPV) or the number of activated cells    (Activated Cells);-   form recognition parameters, such as the form and the size of the    occupied surface (Pattern Recognition).

The aim of the weight estimation module is to combine the values of allparameters to compute a final estimated weight.

Each parameter has an output value that should be correlated to theweight. As there is a dispersion of the parameter value betweendifferent persons having the same weight and between different positionsof one same person, not a discrete weight value but a high probabilityweight range can be assigned to one output parameter value. Accordinglya highest-probability weight range is computed for each parameter valueusing calibration curves (envelopes). The result of all probabilitycalculations is a probability curve from which the final estimatedweight (EW) is deducted.

This is achieved thanks to the so called ‘weight estimation envelopes’.For each parameter, two curves in function of weight are needed: amaximum and a minimum parameter value curve as shown in FIG. 2. Thedefinition of the envelope for a given parameter is the total min-maxvalue spread for the weight range from 0 to 150 kg. This spread isdefined by fitting the data collected during sit-in calibration. Theseat configuration: nominal foam hardness, the most typical trim type,seat back and cushion inclination set to the manufacturer-definednominal value.

After the data collection, the min and max parameter values have to befound. This operation will result in definition of the envelopes. Allweight-points parameter values (min, max) have to be depicted on thesame chart, together with trend lines or interpolation fit, which willdefine the envelopes.

After these calibration curves have been determined, the weightestimation method can be implemented. When the weight estimation modulegets the actual computed parameter values, it superposes the probabilityranges of all those parameters to obtain a final probability curve withas many different probability steps as there are parameters. Using theweight ranges with the highest and the second highest probabilities, itcalculates the final estimated weight.

The calculation of the probability vector may e.g. comprise thefollowing steps:

1. initialize a probability vector whose index is the weight e.g. in 1kg steps (example: one probability value for each 1 kg weight rangebetween 1 and 150 kg) to a 100% value Weight (index) 1 kg 2 kg 3 kg . .. 150 kg Probability 100% 100% 100% 100% 100%

2. For each parameter, multiply the probability (vector value) of theweight where the actual parameter value is outside the correspondingenvelope (>max or <min) by a given border. Weight (index) 1 kg 2 kg 3 kg. . . 150 kg Probability 56.25% 75% 75% . . .% 56.25%

-   3. Find and store the highest and second highest probability value    in the vector.-   4. Calculate the average of the weight points (index of vector)    where the probability is equal to the highest probability.-   5. Calculate the average of the weight points (index of vector)    where the probability is equal or higher than the second highest    probability.-   6. The final estimated weight is the rounded average of the two last    averages.

FIG. 4 shows as an example a probability vector calculated on the basisof six different parameters. The probability lowering method used toexclude low probability ranges could be varied as it doesn't affect thefinal result. The ‘multiply by a given border’ could be replaced by asubtraction, for example, but it must be ensured that the probabilitydoesn't fall below 0% or below the resolution, so that the differentprobability steps still can be distinguished.

In order to eliminate the influence of the ambient temperature in thevehicle on the parameter readings from the sensor, a temperaturecompensation may further be implemented. The aim of thetemperature-weight compensation module is to correct the estimatedweight in function of the temperature. Such a correction should dependon the actual estimated weight and the temperature.

1-8. (Canceled)
 9. A method for the determination of a weight parameterof a seat passenger, comprising the steps of: a) getting a reading of atleast two parameters from an occupancy sensor; b) plotting a probabilityvector, said probability vector showing, for each weight range, theprobability for a seat passenger belonging to said weight range to causesaid readings of said at least two parameters; c) correlating saidweight parameter to the range of values of said probability vector withthe highest probabilities.
 10. The method according to claim 9, whereinsaid step of plotting a probability vector comprises the steps of: a)initializing the probability vector by setting the probability for theentire weight range to a specific value; and b) for each parameter,reducing the probability for each weight value for which the respectiveparameter is outside of calibration curves, said calibration curvesshowing the maximum and minimum parameter values for each weight value.11. The method according to claim 9, wherein said step of correlatingsaid weight parameter to said range of values with the highestprobabilities comprises the steps of: a) calculating an average weightvalue from the range of values of said probability vector with thehighest probability; b) setting said weight parameter to be determinedto equal said average weight value.
 12. The method according to claim 9,wherein said step of correlating said weight parameter to said range ofvalues with the highest probabilities comprises the steps of: a)calculating a first average weight value from the range of values ofsaid probability vector with the highest probability; b) calculating asecond average weight value from the range of values of said probabilityvector for which the probability is equal or higher than a secondhighest probability; c) setting said weight parameter to be determinedto equal a rounded average of said first and second average weightvalue.
 13. The method according to claim 9, wherein said occupancysensor is a pressure sensitive seat sensor or a capacitive sensor. 14.The method for controlling the deployment of a secondary restraintsystem, comprising the steps of: a) determining a weight parameter of aseat passenger by i) getting a reading of at least two parameters froman occupancy sensor; ii) plotting a probability vector, said probabilityvector showing, for each weight range, the probability for a seatpassenger belonging to said weight range to cause said readings of saidat least two parameters; iii) correlating said weight parameter to therange of values of said probability vector with the highestprobabilities; and b) switching the secondary restraint system in adeployment mode which is adapted to a passenger with said weightparameter.
 15. The method according to claim 14, wherein said step ofcorrelating said weight parameter to said range of values with thehighest probabilities comprises the steps of a) calculating an averageweight value from the range of values of said probability vector withthe highest probability; b) setting said weight parameter to bedetermined to equal said average weight value.
 16. The method accordingto claim 14, wherein the said step of correlating said weight parameterto said range of values with the highest probabilities comprises thesteps of a) calculating a first average weight value from the range ofvalues of said probability vector with the highest probability; b)calculating a second average weight value from the range of values ofsaid probability vector for which the probability is equal or higherthan a second highest probability; c) setting said weight parameter tobe determined to equal a rounded average of said first and secondaverage weight value.
 17. The method according to claim 14, wherein saidoccupancy sensor is a pressure sensitive seat sensor and/or a capacitivesensor.
 18. A method for the determination of a weight parameter of aseat passenger; comprising the steps of: a) getting a reading of atleast two parameters from an occupancy sensor; b) plotting a probabilityvector, said probability vector showing, for a plurality of weightranges, the probability for a seat passenger belonging to a weight rangeto cause said readings of said at least two parameters; c) correlatingsaid weight parameter to the range of values of said probability vectorwith the highest probabilities.
 19. The method according to claim 18,wherein said step of plotting a probability vector comprises the stepsof: a) initializing the probability vector by setting the probabilityfor each weight range to a specific value; and b) for each parameter,reducing the probability for each weight value for which the respectiveparameter is outside of calibration curves, said calibration curvesshowing the maximum and minimum parameter values for each weight value.20. The method according to claim 18, wherein said step of correlatingsaid weight parameter to said range of values with the highestprobabilities comprises the steps of a) calculating an average weightvalue from the range of values of said probability vector with thehighest probability; b) setting said weight parameter to be determinedto equal said average weight value.
 21. The method according to claim18, wherein said step of correlating said weight parameter to said rangeof values with the highest probabilities comprises the steps of a)calculating a first average weight value from the range of values ofsaid probability vector with the highest probability; b) calculating asecond average weight value from the range of values of said probabilityvector for which the probability is equal or higher than a secondhighest probability; c) setting said weight parameter to be determinedto equal a rounded average of said first and second average weightvalue.
 22. The method according to claim 18, wherein said occupancysensor is a pressure sensitive seat sensor or a capacitive sensor.
 23. Amethod for controlling the deployment of a secondary restraint system,comprising the steps of: a) determining a weight parameter of a seatpassenger by i) getting a reading of at least two parameters from anoccupancy sensor; ii) plotting a probability vector, said probabilityvector showing, for a plurality of weight ranges, the probability for aseat passenger belonging to a weight range to cause said readings ofsaid at least two parameters; iii) correlating said weight parameter tothe range of values of said probability vector with the highestprobabilities; and b) switching the secondary restraint system in adeployment mode which is adapted to a passenger with said weightparameter.
 24. The method according to claim 19, wherein said step ofcorrelating said weight parameter to said range of values with thehighest probabilities comprises the steps of a) calculating an averageweight value from the range of values of said probability vector withthe highest probability; b) setting said weight parameter to bedetermined to equal said average weight value.
 25. The method accordingto claim 19, wherein said step of correlating said weight parameter tosaid range of values with the highest probabilities comprises the stepsof a) calculating a first average weight value from the range of valuesof said probability vector with the highest probability; b) calculatinga second average weight value from the range of values of saidprobability vector for which the probability is equal or higher than asecond highest probability; c) setting said weight parameter to bedetermined to equal a rounded average of said first and second averageweight value.
 26. The method according to claim 19, wherein saidoccupancy sensor is a pressure sensitive seat sensor and/or a capacitivesensor.